In the field of fault-tolerant control for intelligent vehicle path tracking, the existing methods based on model predictive control (MPC) have limitations. They often assume that the vehicle model parameters are fixed and ignore the impact of dynamic changes in model parameters, which are caused by factors such as vehicle load changes and external environmental interference, on the path tracking accuracy. When the steering system fails, the front wheel steering angle commands calculated by MPC cannot be directly executed and need to be converted into yaw moment control. Although the existing sliding mode yaw moment control methods have strong robustness, the chattering problem affects the stability and execution effect of the control system. To address these issues, this paper proposed a fault-tolerant control scheme for path tracking that integrated MPC with online model parameter updating and adaptive sliding mode control. By updating the MPC model parameters online, the adaptability to the dynamic characteristics of the vehicle was improved. The adaptive sliding mode control was used to reduce the impact of chattering, and the driving torque distribution was optimized in combination with road adhesion constraints to achieve path tracking. The research results show that the tracking accuracy of the proposed algorithm is better than that of the traditional MPC algorithm, and it can achieve more accurate and stable path tracking control, providing new ideas and methods for the development of intelligent vehicle path tracking control technology.